Chapter 2

76

OPTIMIZATION OF LACCASE PRODUCTION BY

PLEUROTUS OSTREATUS IMI 395545 USING

TAGUCHI DOE METHODOLOGY

ABSTRACT

Production of laccase from Pleurotus ostreatus IMI 395545 under submerged

culture condition was optimized by Taguchi orthogonal array (OA) design of

experiment (DOE) methodology. This approach facilitates the study of interactions of

a large number of variables spanned by factors and their settings, with a small number

of experiments, leading to considerable saving in time and cost for the process

optimization. This methodology optimizes number of impact factors and enables to

calculate their interaction in the production of industrial enzymes. Eight factors viz.

glucose, yeast extract, malt extract, inoculum, mineral solution, inducer (CuSO4) and

L-aspargine at three levels and pH at two levels, with an OA layout of L18 (21 x 3

7)

were selected for the proposed experimental design. The Laccase yield obtained from

the 18 sets of fermentation experiments performed with the selected factors and levels

were further processed with Qualitek-4 software. The optimized conditions shared an

enhanced laccase expression of 49.18% (from 429.35 U to 640.5±2.5 U/l). Individual

levels of various factors in 100 ml of optimized medium are pH 6.0, glucose 2 g, yeast

extract 0.5 g, malt extract 0.7 g, mineral solution 20 ml, inoculum 0.5 ml, inducer

1 mM and L-aspargine 2 mg. The contributions of various factors involved in the

optimized medium are as follows glucose 61.14%, pH 20.15%, yeast extract 5.96%,

malt extract 4.21%, CuSO4 3.45%, mineral solution 2.81%, inoculum 1.53% and

L-aspargine 0.71%.

Chapter 2

77

2.1. INTRODUCTION

Laccase is a multicopper blue oxidase capable of oxidizing ortho- and para-

diphenols and aromatic amines by removing an electron and a proton from a hydroxyl

group to form a free radical [Youn et al. 1995]. Laccase plays an important role in the

global carbon cycle and could help in degrading a wide range of xenoaromatics such

as textile dyes [Mester and Tien, 2000], polychlorinated biphenyls, polycyclic

aromatic hydrocarbons, pesticides and synthetic polymers [Bezalel et al., 1997;

Novotny et al., 2000]. Extensive studies made on fungal laccase have proved its

potential in the various field of biotechnology and created a great market demand for

commercial application like waste water detoxification [Eriksson et al., 1990; Shah

and Nerud, 2002], detergent manufacturing and transformation of antibiotics and

steroids [Cohen et al., 2002]. The wide range of application of laccase in the

biotechnological and textile industries creates the need for large amount of enzymes at

low cost to meet the market demand.

The main limitation for the extensive industrial application of laccase is its

high cost. To attain the production of a large amount of enzyme at low cost, media

optimization plays a crucial role. The optimization of fermentation media to generate

a balanced proportion of various nutrients is very important to get optimum microbial

growth and enzyme yield [Elisashvili et al., 2001]. A number of statistical

experimental designs have been studied for the bioprocess optimization. The Taguchi

method of orthogonal array (OA) design of experiments (DOE) involves the study of

any given system by a set of independent variables (factors) over a specific region of

interest (levels) [Mitra, 1998; Roy, 2001]. This methodology simplifies the

complicated optimization bioprocess to the simplest one, which can easily identify the

impact of individual factors, establishing the relationship between variables and

Chapter 2

78

operational conditions. Statistical significance of the experimental results was

validated with ANOVA (analysis of variance) and makes the analysis very precise.

Hence, this methodology was greatly appreciated for less production cost, time

saving, high standard and its systematic process for the optimization of the near

optimum design parameters with limited experimental sets [Kackar, 1985; Taguchi,

1986; Phadke and Dehnad, 1988]. This methodology has been applied for various

bioprocess applications [Jeney et al., 1999; Sreenivas Rao et al., 2003; Venkata Dasu

et al., 2003; Venkata Mohan et al., 2005] and gives excellent results for the

optimization of a few biochemical techniques [Cobb and Clarkson, 1994; Han et al.,

1998].

The present work describes the optimization of submerged culture conditions

for laccase production by newly identified species Pleurotus ostreatus IMI 395545,

using methodological application of Taguchi experimental design.

2.2. MATERIALS AND METHODS

2.2.1. Chemicals

L-aspragine, malt extract and yeast extract were purchased from Himedia,

Mumbai (India). Copper sulfate was purchased from LOBO chemicals, Mumbai

(India). Unless otherwise stated all chemicals were of analytical grade.

2.2.2. Taguchi DOE methodology

Dr. Genichi Taguchi is an engineer who researched extensively at the

Electronic Control Laboratory in Japan on the Design of Experiment techniques

during late 1940s. The Taguchi method involves the establishment of a large number

of experimental situations described as orthogonal arrays (OA) to reduce experimental

errors and to enhance the efficiency and reproducibility of laboratory experiments.

Chapter 2

79

The design of experiments (DOE) methodology by Taguchi orthogonal array (OA), a

factorial-based approach, has gained exceeding importance recently for its application

in optimizing biochemical processes. DOE using the Dr. Genechi Taguchi approach

attempts to improve the quality defined as the consistency of performance, to

optimize the process designs and finished products, to study the effects of multiple

factors (i.e. - variables, parameters, ingredients, etc.) on the performance and solve

production problems by objectively laying out the investigative experiments

[Roy, 1990]. It was introduced in the USA in the early 1980's and can economically

satisfy the needs of problem solving and product/process design optimization projects.

The Taguchi method of DOE analysis helps us to determine the relationship between

variables of medium components and to optimize their concentration, in four different

phases [Lee et al., 1997; Krishna Prasad et al., 2005].

2.2.3. Experimental design

The first phase focused on the composition of the factors to be optimized in

the culture medium that have critical effect on the laccase yield. Fungal laccase

production is influenced by many typical culturing parameters, such as medium

composition, carbon and nitrogen ratio, temperature, pH and aeration ratio

[Niku-Paavola et al., 1990]. Based on the obtained experimental data from our initial

studies, eight factors were selected for the production of laccase by Pleurotus

ostreatus IMI 395545.

The second step was to design the matrix experiment and to define the data

analysis procedure. Taguchi provides many standard OA and corresponding linear

graphs for this purpose [Krishna Prasad et al., 2005]. Three levels of factor variation

were considered and the size of experimentation was represented by symbolic array

L 18. All the factors except for pH (21) were assigned with three levels, with a layout

Chapter 2

80

of L18 (21

x 37) are shown in table 2.1. The total degree of freedom is equal to the

number of trails minus one i.e., 17. In this study, the experiments were carried out in

cotton plugged 250 ml Erlenmeyer flasks containing 100 ml of production medium

glucose (1.0, 1.5 and 2.0 g); yeast extract (0.250, 0.375 and 0.500 g); malt extract

(0.350, 0.525 and 0.700 g); mineral solution (10, 20 and 30 ml); L-aspargine (1.0, 2.0

and 3.0 mg) and CuSO4 (0.5, 1.0 and 1.5 mM). The pH of the production medium was

adjusted to 5.5-6.0 with 2 N HCl prior to sterilization. The composition of the mineral

solution is as follows (g/l): K2HPO4 – 5; NaH2PO4 – 0.1; MgSO4.7H2O – 0.5; CaCl2 –

0.02; FeSO4 .7H2O – 0.01; MnSO4.7H2O – 0.02; ZnSO4.7H2O – 0.02; dissolved in

1liter distilled water. Production medium and inducer were sterilized by autoclaving

for 15 min at 121°C with 15 lbs pressure. The inducer CuSO4 was added to the

production medium after 240 h of cultivation to reduce its effect during the initial

phase of fungal growth during fermentation. The flasks were incubated at 30 C on a

rotary shaker (120 rpm).

2.2.4. Inoculum preparation

The inoculum was prepared by fungal cultivation on a rotary shaker at 150

rpm in 250 ml flasks containing 100 ml basal medium (g/l): glucose – 10;

KH2PO4 – 0.8; NH4NO3 – 2; Na2HPO4 – 0.4; MgSO4.7H2O – 0.5 and yeast extract – 2.

The following microelements were added to the basal medium (g/l) ZnSO4.

7H2O – 0.001; FeSO4.7H2O – 0.005; CaCl2.2H2O – 0.06; CuSO4.7H2O – 0.005;

MnSO4.7H2O – 0.005. After 7 days of fungal cultivation, mycelial pellets were

harvested and homogenized with a waring laboratory blender, three times for 20s with

1-min intervals [Mikiashvili et al., 2006].

Chapter 2

81

2.2.5. Laccase assay

Laccase activity was determined using guaiacol as the substrate according to

the method of Sandhu and Arora [1985]. Kindly refer the previous chapter for details

(1.2.6).

2.2.6. Submerged fermentation experiments

The details of the individual combinations of the 18 experimental trials and

their obtained results for the laccase enzyme activity (U/l) are shown in table 2.2. The

obtained results were analyzed by using “Bigger is better” quality, which was used to

determine the optimum culture condition for maximum enzyme production.

2.2.7. Qualitek-4 software

The Qualitek-4 software (Nutek Inc .MI) allows designing experiments using

any of the L-4 to L-81, L-16 and L-18 (modified) arrays. The experiments can be

designed to include as few as 2 two level factor (L-4) or as many as 63 two-level

factors (L-64). The factors may have two, three or four levels. Qualitek-4 offers two

options for experimental design. The present study selects the automatic design

option, which instructs which array to be use and when. Once the factors and levels

are described, the qualitek-4 software automatically selects the array appropriate

design and places the factors in the correct column. In manual design option, it is

possible to control the design in every step.

2.3. RESULTS

Selection of a suitable substrate at appropriate level is a key factor in

submerged fermentation for laccase production from Pleurotus ostreatus IMI 395545.

Table 2.1 shows the key factors and their levels selected for the optimization process

using Taguchi DOE methodology. Composition of the culture medium and the

Chapter 2

82

quantities of the components determine the production of laccase. Table 2.2 shown

the variation in laccase activity according to the experiments conducted based on the

Taguchi DOE method. The average effect of the factors, along with interaction at the

assigned levels, on the laccase production by Pleurotus ostreatus IMI 395545 are

shown in table 2.3, in which mineral solution shows the highest effect at level 1,

whereas pH, shows the highest effect in level 2. At level 3, glucose has the maximum

effect and it was followed by malt extract. The larger the difference (L2-L1) the

stronger is the influence. Among the factors and their levels studied on the laccase

activity, glucose and pH showed strongest influence (L2-L1) when compared with

other factors, viz. yeast extract, mineral solution, inducer (CuSO4), L-aspargine, malt

extract and inoculum. Increase in the concentrations of factors such as glucose, yeast

extract, malt extract and inoculum has resulted in increase in enzyme production. In

the case of mineral solution, inducer (CuSO4) and L-aspargine, the laccase yield was

higher up to level 2 but subsequent increase in the concentration (level 3) decreased

the laccase yield.

The severity indexes (SI) of the factors interacting at various levels are shown

in table 2.4. The interaction between two factors gives a better view for overall

process analysis. In culture, any individual factor may interact with any or all of the

other factors, creating the possibility of a large number of interactions. The results of

the estimated interaction of the severity indexes of two individual factors at various

levels are as follows. Inoculum and inducer CuSO4 (at levels 3 and 2; column 1)

interaction showed the highest interaction SI (80.74%). Inoculum which has least

impact factor, when combined with inducer CuSO4 showed higher severity index. In

the case of L-asparagine (lower impact factor), the combination with CuSO4 resulted

in higher interaction SI (61.02%). It was interesting to see that the two lowest impact

Chapter 2

83

factors, i.e. those of the inoculum and malt extract in combination gives less

interaction SI (7.55%). The SI of 15.74% was obtained when glucose (the strong

impact factor) was combined with inoculum (with the lowest impact factor).

On the contrary, the SI between pH (second highest impact factor) with glucose (first

strong impact factor) showed least SI (0.17%).

Figure 2.1 shows the variation of laccase activity at chosen levels. Analysis of

variance (ANOVA) was used to analyze the results of the OA experiment and to

determine how much variation was contributed by each factor. From the calculated

ratios (F), it can be seen that all factors and interactions considered in the

experimental design are statistically significant at 90% confidence limit. ANOVA

with the percentage of contribution of each factor with interaction were shown in

table 2.5. Optimum condition and their performance in terms of contribution for

achieving higher laccase yield are shown in table 2.6. The contribution of selected

factors on the laccase production at optimum performance is shown in figure 2.2. The

maximum contribution was given by glucose followed by pH, malt extract, inducer,

yeast extract, mineral solution, inoculum and L-asparagine respectively.

2.4. DISCUSSION

Fungal laccase production is influenced by many typical culturing parameters,

such as medium composition, carbon and nitrogen ratio, temperature, pH and aeration

ratio [Niku-Paavola et al., 1990]. Optimum amounts of carbon and nitrogen in the

medium enable to reach the high activities of extra cellular laccase [Jang et al., 2002;

Kahraman and Gurdal, 2002]. The variables (Table 2.1) selected for the present study

were identified based on a previous report and our laboratory experiments. Table 2.2

shows the variation in laccase activity according to the experiments conducted based

on the Taguchi DOE method.

Chapter 2

84

The production levels were found to be very much dependent on the culture

conditions. Among the factors studied, glucose, yeast extract, malt extract and

inoculum (levels 2 and 3) showed stronger influence when compared to other factors

studied (Table 2.3), whereas mineral solution, inducer (CuSO4) and L-aspargine

increase the laccase activity up to level 2 further increase the concentration will lead

to decreasd the activity (level 3). Increasing the glucose concentration from 5 to 20 g/l

resulted in more than fivefold increase of the laccase activity. A further increase up to

40 g/l did not enhance the laccase activity, but lower activities were obtained [Hao et

al., 2007]. This glucose repression is well known in fungi, and is thought to be an

energy-saving response [Ronne, 1995]. Among the carbon source, glucose is a readily

utilizable substrate which would promote biomass production. It has already been

demonstrated that substrates that are efficiently and rapidly utilized by the organism

results in high levels of laccase activity [Galhaup and Haltrich, 2001].

The culture pH condition is one of the important parameters in fungal

cultivation [Krishna Prasad et al., 2005]. The obtained result shows that laccase yield

was higher at pH 6.0 than at pH 5.5. It was proved that many Pleurotus ostreatus

strains produced the maximum amount of laccase enzyme when the initial pH of the

medium was adjusted to pH 6.0 in submerged culture [Mikiashvili et al., 2006]. The

pH is one of the operational parameters that influence the metabolic activity of the

organism, playing an important role in the protocol optimization for any fermentation

process [Janusz et al., 2007]. However, as per the data in the table 2.4, the SI for

inoculum interaction with the inducer CuSO4 is highest (80.74%) which was then

followed by interaction of inducer CuSO4 with L-aspargine gives 61.02%. This

reveals that, inoculum, inducer CuSO4, and L-aspargine concentrations plays crucial

Chapter 2

85

role in the production of laccase. From the ANOVA table 2.5, we statistically

confirmed that carbon source glucose is the main factor for the production of laccase.

The medium was supplemented with two types of nitrogen sources, yeast

extract and malt extract. Inorganic nitrogen sources supported low levels of laccase

with sufficient biomass production, while the organic nitrogen source gave high

laccase yields with good fungal growth. Yeast extract is one of the best nitrogen

sources that increase the yield of enzyme [Arora and Rampal, 2002]. Moreover, malt

extract is rich in the aromatic amino acids tryptophan and tyrosine. Tryptophan is also

produced de novo by basidiomyetes and it functions as a precursor in the synthesis of

N-substituted aromatic secondary metabolites of fungi [Turner and Aldridge, 1983].

The yield of laccase was increased by supplementation of the medium with an

additional nitrogen sources like amino acid L-aspargine [Janusz et al., 2007].

Nitrogen plays key role in laccase production, the nature and the concentration of

nitrogen in the culture media for growing white-rot fungi are essential for laccase

production [Galhaup et al., 2002a]. Usually high nitrogen concentration is required

for optimal laccase production [Gianfreda et al., 1999]. It was evident that sufficient

amounts of carbon and nitrogen in the medium increase the productivity of laccase

two times higher than that obtained on the original Lindbergh-Holm medium as a

control [Arora and Rampal, 2002].

Besides the composition of culture media, including the carbon substrates, all

micronutrient are determinant for cell growth and specific enzyme production [Xavier

et al., 2007]. The time point of cupric ions supplementation and cupric ions

concentration were important for obtaining increased laccase activity [Janusz et al.,

2007]. Another important role for the copper is regulating the laccase gene

transcription [Palmieri et al., 2000; Galhaup and Haltrich, 2001] at the same time

Chapter 2

86

copper concentration in culture media had a clear effect during culture at low nitrogen

concentration, rather than the culture with high nitrogen content [Cavallazzi et al.,

2005]. This argument was well agreed with the results drawn by Schlosser et al.,

[1997].

According to the figure 2.1, glucose and pH plays an important role in

influencing the laccase production. Furthermore, the studied strain produced increased

laccase titers without the addition to the culture medium of phenolic and aromatic

inducer related to lignin or lignin derivatives, which are often used to stimulate

enzyme formation in most other fungal species [Leonowicz et al., 1997]. Copper is an

essential micro-nutrient for most living organisms, and copper requirements by

microorganisms are usually satisfied by very low concentrations of the metal, in order

of 1-10 mM. However, copper present in higher concentration is extremely toxic to

microbial cells [Labbe and Thiele, 1997], although some copper-tolerant fungi had

already been described [De Groot and Woodward, 1999]. As per our study increase of

copper sulphate increase the laccase production up to level 2 (1.0 mM) further

increase to 1.5 mM (Level 3) decrease the production of laccase it may due to the

toxic effect of the metal in the medium.

The contribution of individual factors is the key factors for the efficiency of

fermentation process. According to the figure 2.2, the higher levels of laccase activity

can be achieved with obtained optimization culture conditions for 100 ml: pH 6.0,

glucose 2 g, yeast extract 0.5 g, malt extract 0.7 g, mineral solution 20 ml, inoculum

0.5 ml, inducer 1 mM and L-asparagine 2 mg. It is evident from the table 2.6, that

upon considering the optimum culture condition from the designed experiments, the

laccase yield can be increased from 429.35 U to 737.74 U/l (Predicted value by

Qualitek-4 software) i.e. over all 71.8% increase in enzyme production can be

Chapter 2

87

achieved. To validate the proposed experimental methodology, production

experiments were conducted by applying the obtained optimized culture condition as

per the table 2.6. The obtained results confirmed an enhanced laccase yield of

640.5±2.5 U from 429.35 U (49.18% increased in laccase yield) with the Taguchi

DOE optimized culture condition.

2.5. CONCLUSION

It can be concluded that the optimization of production medium is one of the

key factors to maximize the yield of laccase. Traditional methods of optimization

involved changing one independent variable while fixing the others at a certain level.

This single-dimensional search is laborious, time-consuming and incapable of

reaching a true optimum due to interactions among variables. The Taguchi approach

of OA design of experiment constitutes a simple methodology that selects the best

conditions producing consistent performance. Hence, the production medium for

laccase was first optimized by the Taguchi DOE methodology. In the case of

inducers and enhancers further detailed studies has to be conducted to improve the

yield of laccase by identifying the right concentration and the time point for giving the

dose for the subjected strain. This step by step approach may be very helpful to

increase the yield of laccase in the case of new strains.

Chapter 2

88

Figure 2.1. Relative influence of factors and contributions.

Figure 2.2. Optimum performance with the major contributions.

pH Glucose Yeast extract

Malt extract Mineral solution Inoculum

CuSO4 L-aspargine Error

400

500

600

700

800

Av

erag

e

Glu

cose

pH

Mal

t ex

trac

t

Yea

st e

xtr

act

Min

eral

so

luti

on

Inocu

lum

Am

ino

aci

d

Factors

Lac

case

act

ivit

y (

U/l

)

Amino

acid Inoculum

Mineral

solution Yeast

Extract CuSO4

Malt

Extract pH

Glucose

Average

CuSO4

CuSO4

Cu

SO

4

C u S O 4

Chapter 2

89

Table 2.1. Selected culture condition factors and assigned levels

Factors Level 1 Level 2 Level 3

pH 5.5 6.0 -

Glucose (g) 1.0 1.5 2.0

Yeast extract (g) 0.250 0.375 0.500

Malt extract (g) 0.350 0.525 0.700

Mineral solution (ml) 10 20 30

Inoculum (ml) 0.1 0.2 0.5

CuSO4 (mM) 0.5 1.0 1.5

L-asparagine (mg) 1.0 2.0 3.0

Chapter 2

90

Table 2.2. L18 (21

×37) orthogonal array of designed experiment

Experiment

No

Column Laccase

activity (U/l)* 1 2 3 4 5 6 7 8

1. 1 1 1 1 1 1 1 1 125.75 ± 0.3

2. 1 1 2 2 2 2 2 2 310.80 ± 0.4

3. 1 1 3 3 3 3 3 3 284.20 ± 0.6

4. 1 2 1 1 2 2 3 3 340.50 ± 0.8

5. 1 2 2 2 3 3 1 1 390.85 ± 0.7

6. 1 2 3 3 1 1 2 2 495.50 ± 0.2

7. 1 3 1 2 1 3 2 3 471.75 ± 0.3

8. 1 3 2 3 2 1 3 1 500.50 ± 0.6

9. 1 3 3 1 3 2 1 2 445.00 ± 0.5

10. 2 1 1 3 3 2 2 1 335.70 ± 0.5

11. 2 1 2 1 1 3 3 2 345.50 ± 0.4

12. 2 1 3 2 2 1 1 3 381.15 ± 0.7

13. 2 2 1 2 3 1 3 2 431.55 ± 0.4

14. 2 2 2 3 1 2 1 3 541.65 ± 0.3

15. 2 2 3 1 2 3 2 1 591.70 ± 0.6

16. 2 3 1 3 2 3 2 2 621.00 ± 0.7

17. 2 3 2 1 3 1 2 3 563.15 ± 0.2

18. 2 3 3 2 1 2 3 1 552.10 ± 0.5

*Mean SD, n =3

Chapter 2

91

Table 2.3. Main effects of selected factors

Factor Level 1 Level 2 Level 3 L2-L1

pH 373.872 484.833 110.961

Glucose (g) 297.183 465.291 525.583 168.108

Yeast extract (g) 387.708 442.075 458.274 54.366

Malt extract (g) 401.933 423.033 463.091 21.1

Mineral solution (ml) 422.041 457.608 408.408 35.567

Inoculum (ml) 416.266 420.958 450.833 4.692

CuSO4 (mM) 417.566 461.433 409.058 43.867

L-aspargine (mg) 416.1 441.558 430.399 25.457

Chapter 2

92

Table 2.4. Estimated interaction of severity index for different factors

Factors Columns SI

(%)

Reversed

column

Levels

Inoculum × CuSO4 6 × 7 80.74 1 [3,2]

CuSO4 × L-aspargine 7 × 8 61.02 15 [1,2]

Malt extract × Mineral solution 4 × 5 60.96 1 [3,2]

Inoculum × L-aspargine 5 × 7 60.43 2 [2,1]

Yeast extract × L-aspargine 3 × 8 60.73 14 [3,1]

Mineral solution × CuSO4 5 × 7 59.67 2 [2,1]

Mineral solution × Inoculum 5 × 6 59.41 3 [2,3]

Yeast extract× Malt extract 3 × 4 55.91 7 [2,3]

Yeast extract × Mineral solution 3 × 5 48.86 6 [3,1]

Malt Extract × CuSO4 4 × 7 48.44 3 [3,1]

Mineral solution × L-aspargine 5 × 8 39.04 13 [2,1]

Malt extract × L-aspargine 4 × 8 34.28 12 [3,2]

pH × Malt extract 1 × 4 33.75 5 [2,1]

pH × L-aspargine 1 × 8 33.66 9 [2,3]

pH × CuSO4 1 × 7 31.75 6 [2,1]

Yeast extract x Inoculum 3 × 6 30.82 5 [1,3]

Glucose × Malt extract 2 × 4 25.41 6 [3,3]

Glucose × Mineral solution 2 × 5 25.04 7 [3,2]

Glucose × L-aspargine 2 × 8 20.7 10 [3,2]

Yeast extract × CuSO4 3 × 7 17.51 4 [3,2]

pH × Yeast extract 1 × 3 17.21 2 [2,3]

Glucose × Inoculum 2 × 6 15.74 4 [3,3]

pH × Mineral solution 1 × 5 9.56 4 [2,2]

Chapter 2

93

pH × Inoculum 1 × 6 8.55 7 [2,3]

Malt extract × Inoculum 4 × 6 7.55 2 [3,1]

Glucose × Yeast extract 2 × 3 2.72 1 [3,1]

Glucose × CuSO4 2 × 7 1.3 5 [2,2]

pH × Glucose 1 × 2 0.17 3 [2,3]

Table 2.5. Analysis of Variance (ANOVA)

Factors DOF Sums of

squares Variance F Ratio Pure sum

Percentage

(%)

pH 1 110811.437 110811.437 18136.758 110805.327 20.149

Glucose (g) 2 336247.351 168123.675 27517.181 336235.132 61.141

Yeast extract (g) 2 32791.733 16395.866 2683.548 32779.513 5.96

Malt extract (g) 2 23160.959 11580.479 1895.403 23148.74 4.209

Mineral solution (ml) 2 15486.415 7743.207 1267.348 15474.195 2.813

Inoculum (ml) 2 8437.941 4218.97 690.528 8425.721 1.532

CuSO4 (mM) 2 18959.381 9479.69 1551.562 18947.161 3.445

L-aspargine (mg) 2 3908.608 1954.304 319.865 3896.388 0.708

Other / error 20 122.195 6.109 - - 0.043

Total 35 549926.024 - - - 100.00

Chapter 2

94

Table 2.6. Optimum culture condition and their contribution

Factors Values Level Contribution

pH 6.0 2 55.48

Glucose (g) 2.0 3 96.23

Yeast Extract (g) 0.5 3 28.922

Malt Extract (g) 0.7 3 33.738

Mineral solution (ml) 20 2 28.255

Inoculum (ml) 0.5 3 21.480

CuSO4 (mM) 1.0 2 32.08

L-aspargine (mg) 2 2 12.209

Total contribution from all factors = 308.394

Current grand average performance = 429.352

Expected result at optimum condition = 737.746.